CN108257222A - The automatic blending algorithm of steel stove converter three-dimensional laser point cloud - Google Patents
The automatic blending algorithm of steel stove converter three-dimensional laser point cloud Download PDFInfo
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- CN108257222A CN108257222A CN201810093755.9A CN201810093755A CN108257222A CN 108257222 A CN108257222 A CN 108257222A CN 201810093755 A CN201810093755 A CN 201810093755A CN 108257222 A CN108257222 A CN 108257222A
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- 239000000284 extract Substances 0.000 claims description 7
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Abstract
The present invention relates to a kind of automatic blending algorithm of steel stove converter three-dimensional laser point cloud, including the extraction of converter area data, converter feature extraction, converter data fusion.The effect of invention is：The present invention can be used for monitoring steel stove converter lining in real time, detects the erosion condition of converter lining automatically in the case where not stopping work, can prevent the generation of converter bleed-out accident, ensures the safety in production of steel stove；Lining repairing can be rationally instructed, reduces the waste of furnace charge, extends the service life of furnace lining.
Description
Technical field
The present invention relates to field of metallurgy, especially a kind of automatic blending algorithm of steel stove converter three-dimensional laser point cloud.
Background technology
Steel stove converter lining thickness or ablation situation, are of great significance to the safety in production of steel stove.Due to the life of steel mill
Environmental restrictions (high temperature, dust, vibration etc.) are produced, to the behaviour in service of converter lining in operational process, for a long time, are lacked effectively
On-line monitoring.Traditional processing method is in the case where steel mill stops work, and steel stove liner after cooling is carried out artificial
Detection.The deficiency of this method mainly includes：The economic loss that shut-down is brought, energy loss caused by steel stove cooling and heating
And the efficiency of environmental pollution and detection, accuracy or precision are relatively low.With the development of the technologies such as laser radar, also there is the country
Foreign minister's shutout commercial lasers scanning device, is monitored converter and furnace lining in the case where steel stove is not stopped work.Its groundwork
Principle is, using Laser Scanning Equipment, never carries out three-dimensional imaging to converter and furnace lining to angle (due to the factors shadow such as blocking
It rings, needs " to visualize " gathered data from different angles)；The multi-angle being collected into (survey station) data are registrated, are merged,
It analyzes it again.It is known that more survey station data fusions using two ways, (a) registration fusion by hand, (b) installation
After target or other markers, data are merged automatically.Fusion needs to select by hand between different survey station data same by hand
Name feature, data fusion is carried out after calculating transformation parameter again；In addition to it cannot meet requirement of real time, data fusion precision is operated
Person influences.After target is installed, can to a certain extent be solved using the known features of marker as with reference to being merged automatically
The low efficiency problem merged by hand.But, on the one hand, the installation (reconnaissance, arrangement, connection etc.) of target object is relatively difficult, and its
Service life is shorter, on the other hand, deformation, pollution of the marker under high temperature, dust atmosphere etc. also can to marker detection,
Data fusion precision etc., which is brought, to be seriously affected.
Invention content
It is a kind of not to any transformation of original production environment progress the invention solves the shortcomings that the above-mentioned prior art, providing
In the case of, structure feature is automatically extracted, and the steel stove converter three-dimensional laser point cloud for merging more scape point cloud datas merges calculation automatically
Method.
The present invention solves the technical solution that its technical problem uses：This steel stove converter three-dimensional laser point cloud merges calculation automatically
Method includes the following steps：
1) for the multiple observation data of same position, method is directly differed using cloud or scanning figure differs method, is turned
Stove area data is extracted；
2) pass through furnace shell furnace lining data separating, the tracking of furnace shell outer boundary, fire door extraction, fire door plane fitting step, progress
Converter feature extraction；
3) using the fire door plane of reference data as reference, after data are translated, rotate, then iteration refinement, progress converter number
According to fusion, final fusion results are generated.
Preferably, the described cloud directly method of differing is, on the basis of reference data, by other observation data and reference number
According to subtracting each other；Wherein, it is contemplated that the factors such as observation error limit the judgement setting tolerance of same place；Difference result is recycled and is connected
It connects componential analysis and rejects target fine crushing, finally extract converter region.
Preferably, the scanning figure difference method is that three-dimensional point cloud rectangular co-ordinate is converted to spherical coordinate system, recycles column
Face is projected as scanning figure；The larger image of selection differences carries out difference, makes an uproar to the filter of difference result, thresholding, reflation recovery section
Divide the fringe region being corroded；The convex closure of salient angle point set is calculated, forms converter area mask；With this mask to original point cloud data
It is cut, extracts converter area data；To improve extraction accuracy, coordinator analytic approach is recycled to pick area mask result
Except component fine crushing, final converter point cloud data is formed.
Preferably, furnace shell furnace lining data separating is analyzed based on coordinator, i.e., point cloud data is carried out by Euclidean distance
It clusters, be divided into different neighboring regions, select furnace shell component therein；The tracking of furnace shell outer boundary based on algorithm of convex hull, i.e., from
Seed point in furnace shell point cloud data starts, and the point in minimum corner stitch for selecting flying spot inswept is as next boundary
Point/line, iteration are proceeded as described above until starting point is returned to；Fire door is extracted then with the length in the furnace shell point cloud data institute structure triangulation network
Based on the triangle of side, region growth is carried out, until it, which abuts the triangle length of side, is less than threshold value, region growth results are
Fire door region, boundary point are fire door boundary point；The fitting of fire door planar chip then based on fire door boundary point, carries out minimum two
Multiply plane fitting, Calculation Plane parameter.
Preferably, fire door point cloud rotary course centered on reference point, is directly based upon the rotation of fire door plane normal direction angle；
Point cloud iteration refinement process based on iteration abutment points algorithm i.e. gradually calculate abutment points pair between transformation relation and data melt
Close error；It calculates again and data is applied with the fusion error after transformation, it is to terminate iterative process that error, which is less than given threshold value, twice.
The effect of invention is：The present invention can be used for monitoring steel stove converter lining in real time, be examined automatically in the case where not stopping work
The erosion condition of converter lining is surveyed, the generation of converter bleed-out accident can be prevented, ensure the safety in production of steel stove；Can rationally it refer to
Lining repairing is led, reduces the waste of furnace charge, extends the service life of furnace lining.
Description of the drawings
Fig. 1 is the algorithm process block diagram of the present invention；
Fig. 2 is a coordinate system used in cloud scanning figure and projection (rectangular co-ordinate to spherical coordinates)；
Fig. 3 is converter data in scanning figure difference method (point cloud) extraction flow chart；
Fig. 4 is converter feature extraction block diagram；
Fig. 5 is point converter cloud data fusion process figure；
Fig. 6 is steel stove three-dimensional laser point cloud raw-data map (fixed survey station, converter rotation)；
Fig. 7 is the range sweep figure of corresponding three-dimensional laser point cloud data in Fig. 6；
Fig. 8 is converter area mask generating process (after image difference, binaryzation filters, then calculates convex closure)；
Fig. 9 is furnace shell point cloud chart (left, to project to scanning plane) and converter approximate boundaries figure (right side)；
Figure 10 is fire door Boundary Extraction figure (left side is fire door region, and the right side is corresponding fire door boundary point)；
Figure 11 is converter iteration fusion results figure.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings：
Embodiment：
As shown in Figure 1, the process flow of this algorithm includes three major parts, the extraction of converter area data, converter feature
Extraction and converter point cloud data fusion.
To the multiple observation data (converter different rotation angle) of same position, converter extracted region uses two kinds of not Tongfangs
Formula：(a) point cloud directly differs method, (b) scanning figure difference method.
Point cloud directly differ rule directly with reference data (what is selected in the multi-group data observed goes out a certain data) for
After benchmark, other observation data are subtracted each other with reference data；Wherein, it is contemplated that the factors such as observation error, the judgement to same place
Certain tolerance is set to limit (3d distance thresholds), the nominal accuracy of tolerance here limit and instrument and measurement point are away from related, here
Range substantially 0.5~2cm (instrument nominal accuracy 3mm@10m, ranging about 10m)；Coordinator is recycled to difference result
Analysis method (connected component analysis) rejects target fine crushing, finally extracts converter region (point cloud).
Three-dimensional point cloud is converted to range sweep figure by scanning figure difference rule, that is, rectangular co-ordinate is converted into spherical coordinate system,
Recycling cylindrical surface projecting is scanning figure (as shown in Figure 2).The specific steps are：Each data are aligned (by scanning angle first
Range seeks common ground), scanning figure (depth/distance) uses (θ, 90- φ, r), and wherein angle is according to input value uniform quantization, distance
Value is quantified as gray value；Next, and the larger image progress difference of selection differences (during two groups of data acquisitions, steel stove converter rotation angle
The difference of degree, that is, corresponding data (imaging) difference, the corner to differ greatly i.e. between the two is big, is conducive to protrude aberration
Different detection, but as long as there is (corner) difference, the difference of imaging can detected), making an uproar to the filter of difference result, (morphology is calculated
Son, the corrosion of square structure element), thresholding (binaryzation), reflation (morphological operator, square structure element expansion) recovery section
Divide the fringe region being corroded；Again, the convex closure (influence for eliminating the inactive areas such as gap) of salient angle point set is calculated, forms converter
Area mask finally, cuts original point cloud data with this mask, extracts converter region (or part) data.To improve
Extraction accuracy recycles area mask result coordinator analysis to reject component fine crushing, forms final converter point cloud data.
Specific process flow is with reference to figure 3.
Fire door plane (piece) is characterized as the converter of point cloud data fusion, and processing step includes：Furnace shell furnace lining data point
From furnace shell outer boundary tracking, fire door extracts, and fire door plane (piece) fitting, specific steps are shown in Fig. 4.Furnace shell furnace lining data separating base
It is analyzed in coordinator, that is, point cloud data by Euclidean distance is clustered, is divided into different neighboring regions, selection is wherein
Furnace shell component.The tracking of furnace shell outer boundary is then based on convex closure innovatory algorithm, that is, (selection) is from the seed point in furnace shell point cloud data
(most right coordinate points under most) start, and the point (counter clockwise direction) in the minimum corner stitch for selecting flying spot inswept is as next
Boundary point (line), iteration is proceeded as described above until starting point is returned to.Fire door is extracted then with the furnace shell point cloud data institute structure triangulation network
In long side triangle based on (hole seed region), carry out region growth, be less than threshold value until it abuts triangle length of side
Until；Region growth results are fire door region, and boundary point is fire door boundary point.Fire door planar chip is fitted then with fire door side
Based on boundary's point, least square plane fitting, Calculation Plane (equation) parameter are carried out.
Point converter cloud data fusion process then using the fire door plane of reference data as refer to (geometric center, plane normal direction),
After data are translated, rotating, then iteration refinement, generate final fusion results (Fig. 5).Wherein fire door point cloud rotary course, then
Centered on reference point, it is directly based upon the rotation of fire door plane normal direction angle, that is, use the spin matrix of following form：
R=I+ (sin θ) K+ (1-cos θ) K2,
Wherein, I is 3 unit matrixs of 3X, antisymmetric matrix
Point cloud iteration refinement process is then based on iteration abutment points algorithm (Iterative Closest Points, ICP),
That is, gradually calculate transformation relation (transformation matrix of the abutment points between (feature of the same name)：Rotation, translation etc.) and data melt
Close error；The fusion error after data are applied with transformation (transformation matrix in the preceding an iteration of application) is calculated again, twice error
Iterative process is terminated less than given threshold value.
Below with certain steel mill's embodiment explanation.Wherein three station laser point cloud datas (initial data) are as shown in fig. 6, be converted to
Range sweep is as shown in fig. 7, converter extracted region is shown in Fig. 8.Converter characteristic extraction procedure includes fire door detection, frontier tracing (figure
9), fire door border points extraction (Figure 10), fire door parameter fitting etc., parameter fitting are based on least square method.Fusion process is then in
Between on the basis of group (survey station) data, other data are moved to the origin position of reference data, then with corresponding fire door face normal direction
Angle is rotated, and the result that data are slightly aligned carries out feature iteration of the same name (or closest approach iteration) refinement again, and generation is final
Fusion results (Figure 11).
Based on the automatic blending algorithm of converter three dimensional point cloud of the present invention, any transformation is not carried out to former production environment
(not adding label, target etc., no auxiliary data) without manual intervention, automatically, high-precision, merges, splices more scapes in real time
Laser point cloud data.Algorithm can be applied to steel mill, solve automatic data collection, reconstruction, analysis and the monitoring problem of steel stove inner wall.
In addition to applied to the special scenes, algorithm is applied also for such as more survey stations, multi-source terrain data fusion, indoor scene fusion or literary
Object three-dimensional reconstruction etc..
The accuracy value of this algorithm data fusion results is grade (average middle error<1mm), fusion iterations are few (flat
Respectively less than 5 times are to converge on global optimum), fast convergence rate is efficient.(algorithm) of the invention efficiently solves other three-dimensionals
The defects of point cloud data fusion algorithm (locally optimal solution, less efficient etc.), this is to realize for the first time in the world.
In addition to the implementation, the present invention can also have other embodiment.It is all to use equivalent substitution or equivalent transformation shape
Into technical solution, all fall within the present invention claims protection domain.
Claims (5)
1. a kind of automatic blending algorithm of steel stove converter three-dimensional laser point cloud, includes the following steps：
1) for the multiple observation data of same position, method is directly differed using cloud or scanning figure differs method, carries out converter area
Numeric field data is extracted；
2) pass through furnace shell furnace lining data separating, the tracking of furnace shell outer boundary, fire door extraction, fire door plane fitting, progress converter feature
Extraction；
3) using the fire door plane of reference data as reference, after data are translated, rotate, then iteration refinement, progress converter data are melted
It closes, generates final fusion results.
2. the automatic blending algorithm of steel stove converter three-dimensional laser point cloud according to claim 1, it is characterized in that：Described cloud is straight
Connecing difference method is, on the basis of reference data, other observation data are subtracted each other with reference data；Wherein, it is contemplated that observation error
Etc. factors, the judgement of same place setting tolerance is limited；Coordinator analytic approach is recycled to reject target fine crushing difference result, most
After extract converter region.
3. the automatic blending algorithm of steel stove converter three-dimensional laser point cloud according to claim 1, it is characterized in that：The scanning figure
Difference method is three-dimensional point cloud rectangular co-ordinate to be converted to spherical coordinate system, recycling cylindrical surface projecting is scanning figure；Selection differences are larger
Image carry out difference, to difference result filter make an uproar, thresholding, the fringe region that reflation recovered part is corroded；Calculate salient angle
The convex closure of point set forms converter area mask；Original point cloud data is cut with this mask, extracts converter area data；
To improve extraction accuracy, coordinator analytic approach is recycled to reject component fine crushing area mask result, form final converter
Point cloud data.
4. the automatic blending algorithm of steel stove converter three-dimensional laser point cloud according to claim 1, it is characterized in that：Furnace shell furnace lining number
It is analyzed according to separation based on coordinator, i.e., point cloud data by Euclidean distance is clustered, is divided into different neighboring regions, selected
Select furnace shell component therein；The tracking of furnace shell outer boundary is opened based on convex closure innovatory algorithm from the seed point in furnace shell point cloud data
Begin, as next boundary point/line, iteration proceeds as described above back the point in minimum corner stitch for selecting flying spot inswept
Until starting point；Fire door extraction then based on the long side triangle in the furnace shell point cloud data institute structure triangulation network, carries out region increasing
Long, until it, which abuts the triangle length of side, is less than threshold value, region growth results are fire door region, and boundary point is fire door
Boundary point；The fitting of fire door planar chip then based on fire door boundary point, carries out least square plane fitting, Calculation Plane parameter.
5. the automatic blending algorithm of steel stove converter three-dimensional laser point cloud according to claim 1, it is characterized in that：Fire door point cloud revolves
Journey is turned over centered on reference point, is directly based upon the rotation of fire door plane normal direction angle；It is adjacent that point cloud iteration refinement process is based on iteration
Contact algorithm gradually calculates the transformation relation and data fusion error between abutment points pair；It calculates to apply data again and become
Fusion error after changing, it is to terminate iterative process that error, which is less than given threshold value, twice.
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| CN111507340A (en) * | 2020-04-16 | 2020-08-07 | 北京深测科技有限公司 | Target point cloud data extraction method based on three-dimensional point cloud data |
| CN113345007A (en) * | 2021-05-12 | 2021-09-03 | 清华大学 | Automatic positioning method and device for converter mouth, electronic equipment and storage medium |
| CN116240328A (en) * | 2021-12-03 | 2023-06-09 | 清华大学 | Converter steelmaking end point control method, system, device, equipment, medium and product |
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Effective date of registration: 20240625 Address after: Room E10, No. 889 Jiangcheng Road, Shangcheng District, Hangzhou City, Zhejiang Province, 310002 Patentee after: XIAN HENG INTERNATIONAL TECHNOLOGY CO.,LTD. Country or region after: China Address before: Room 1501, 15th Floor, No. 217 Wujiang Road, Shangcheng District, Hangzhou City, Zhejiang Province, 310000 Patentee before: HANGZHOU ZHONGKE TIANWEI TECHNOLOGY Co.,Ltd. Country or region before: China |